Random Sampling Explained: What Is Random Sampling?

Random sampling is a method of data collection and analysis designed to select a representative sample of respondents out of a larger population. Random sampling is named as such because the data set is chosen via random selection, where every member of the population has an equal probability of being selected. This classifies it as a probability sampling method. The opposite is a non-probability sampling method, where not all members of a population have an equal chance of being chosen. When properly conducted, random sampling results should be representative of the population at large. Researchers study those respondents and then extrapolate a set of inferences about the entire population based on data analysis of those sampled.

Random sampling is characterized by the following features:

1. 1. Respondents are randomly chosen from a broader sampling frame. In a random sample, a randomly chosen subset of the larger population is selected for study. The methodology for random selection varies. In a simple random sample, a researcher might assign a number to every member of the population and then use a random number generator or a random number table to pick respondents. Phone surveys often work this way, with phone numbers chosen at random.
2. 2. Researchers use data from sample groups to make inferences about the whole population. Statisticians typically regard random sampling strategies as an accurate snapshot of the total population.
3. 3. Sample sizes must be adjusted for population size. To create a scientifically useful study, surveys of large populations must contain a sufficient number of respondents to assure a truly random and representative sample. For instance, a study of New York City residents would require a notably larger sample size than a study of Muncie, Indiana, residents.

Random sampling has myriad uses in both science and market research. Some of its manifestations include phone surveys, political polling, epidemiology and tracking disease, gene pool mapping, and tracking public opinion. Researchers may also use random sampling methods to track academic performance across a region or school district.

There are four main types of random sampling techniques that statisticians use.

1. 1. Simple random sampling: The simple random sampling method chooses respondents from a sample frame by using random techniques. The most common among these is random number generation where every member of the sample frame is assigned a number and certain numbers are picked at random.
2. 2. Systematic sampling: This type of probability sampling streamlines simple random sampling by using fixed intervals to pick respondents from an entire population. Instead of random number tables or random number generators, a statistician using systematic random sampling might survey every nth person in a population. For instance, they could assign a number to every person in the sample frame but only pick people whose numbers end in the digit five.
3. 3. Cluster sampling: The cluster sampling process starts by dividing a total population into smaller groups known as clusters. Then, one of these clusters is chosen for study. The key to this type of sampling is that each of these clusters is intended to have near-identical characteristics. For instance, if a large company has seven main offices, one of those offices would be chosen for study and the results would be extrapolated to describe the company at large.
4. 4. Stratified sampling: Like cluster sampling, stratified sampling breaks potential respondents into subgroups. The difference is that in stratified sampling, these groups are intentionally homogenous. Every member of a stratum shares a common trait (such as gender, language spoken, or highest educational degree obtained). What's more, they cannot fall into more than one strata. Researchers use stratified sampling when the available sample frame may not be representative of the total population they want to study. By breaking the sample frame into homogenous demographics, and then weighting certain demographics to represent the real-world population, statisticians can create more relevant and useful data sets.

Random sampling is considered a gold standard for statistical analysis. It has a much lower chance of sampling error than non-probability sampling methods, such as convenience sampling where respondents are chosen based on their availability to the researcher. This representative accuracy is the primary advantage of random sampling.

The main disadvantage to random sampling is that it only works if a sample frame truly represents the total population. The respondents must also be chosen at random, which can be more difficult than it seems. To truly recruit a representative random sample, statisticians must very carefully plan their outreach and make sure that no groups are accidentally over-represented or under-represented. This can be time-consuming and expensive, which is why some researchers fall back on convenience sampling or other non-probability sampling methods.

Get the MasterClass Annual Membership for exclusive access to video lessons taught by science luminaries, including Terence Tao, Bill Nye, Neil deGrasse Tyson, Chris Hadfield, Jane Goodall, and more.